Courses - Students are
required to complete 6 of the following courses:
Core Courses (mandatory*):
520 | PROBLEM BASED LEARNING IN BIOINFORMATICS
The problem-based learning course
will develop students' ability to exchange ideas in small groups
focused on real but
simplified problems in bioinformatics.
Problems will be carefully selected to cover all aspects of bioinformatics
core curriculum requires that a student has a strong
background in bioinformatics. In addition, it is highly recommended
that student has also taken BIOF 501A.
501A | SPECIAL TOPICS IN BIOINFORMATICS
This discussion-based Bioinformatics course will expose students
to the latest developments in Bioinformatics analysis and
algorithms. It will run in conjunction
with the VanBug Seminar Series, in which the students will have
the opportunity to
meet and discuss their work with guest speakers, both local
and international scientists.
Elective: Choose either one of 1A/1B OR 2)
711 | BIOINFORMATICS ALGORITHMS - may be a substitute
for CMPT 881
This is an introductory level
graduate course on fundamental computational techniques which have
applied to key problems in bioinformatics.
Particular problem areas of interest include sequence alignment
and search, motif
discovery, molecular structure prediction, phylogenetics,
biomolecular interactions and cellular networks. We will cover
various computational tools ranging from ones which are combinatorial
in nature, such as dynamic programming, index
structures, approximation algorithms, and randomized algorithms
to those which are statistical such as
maximization and Gibbs sampling.
CPSC 545 | ALGORITHMS FOR
BIOINFORMATICS - may be a substitute for CMPT 771
This graduate level course in computer-science that
focuses on the algorithms that are currently in Bioinformatics.
sequence alignment, gene prediction
and sequence annotation, RNA and protein structure prediction and
analysis. The aim of this course is to give you detailed
understanding of the existing algorithms and to prepare you to
develop you own applications and algorithms. The course is
meant to be very interactive in style
and will involve course-
work on projects. You should be comfortable with basic mathematical
reasoning, have a good understanding of the
principles of molecular biology and be confident programming
in a higher-level language such as C, C++ or Java.
Due to the interactive nature of the course, enrollment is restricted
to a small number of dedicated students.
CPSC 445 may be substituted
for CPSC 545 if the student does not have a strong computational
STAT 540/ BIOF 540| STATISTICAL METHODS
FOR HIGH DIMENSIONAL BIOLOGY
This course will cover quantitative
problems arising from current research. We focus on areas in which
approach provides a powerful tool for separating signal from
noise. Students will learn to translate genomic research
questions into well-defined computational problems. Solutions
and algorithms are found which are both theoretically sound
and practical to implement. Selected topics: gene expression
analysis, analysis of tissue and protein arrays, sequence
alignment and comparison, Hidden Markov Models.
* If you have already taken any of these courses as an
undergraduate or have taken equivalent material at another University,
you are not
required to repeat the material, rather choose an additional
elective to make up the requirement of 6 courses needed for graduate
credits). Please note that University policy specifies that
no course credit can be awarded to a student towards
graduate studies credits for
courses taken before enrollement in graduate school.
419 (cross-listed with CMPT 829)| BIOMEDICAL IMAGE
This course is designed to give students the knowledge
needed to understand, develop, and use software and algorithms
on medical image data, to extract useful clinical information.
It may be viewed as a course in image processing and computer
vision adapted to 3D (volumetric) and more complex medical
images (such as MRI or CAT scans), with health-related application.
Details at: http://www.cs.sfu.ca/~hamarneh/419_829.html
705 | DESIGN AND ANALYSIS OF ALGORITHMS
726 | MACHINE LEARNING
Learning is the study of computer algorithms that improve automatically
through experience. It is one of the most exciting aspects of
artificial intelligence, and is the basis for many of its industrial
applications. It is the preferred framework for many applications,
such as face detection (auto-focus in your digital camera), hand-written
digit recognition, speech recognition, and credit card fraud detection.
741 | DATA MINING
Covers essential techniques for searching and mining
large databases, in particular, biological databases, text databases
and business databases. Topics: database systems, association
analysis, classification and prediction, cluster analysis, searching
and mining sequence & multidimensional data, and their applications.
880 | MEDICAL IMAGE ANALYSIS
course focuses on discussing recent research papers on medical
image analysis., including topics on medical imaging, image processing/
filtering, image segmentation, image registration and shape modeling,
in the context of different applications such as computer aided
diagnosis and statistical shape analysis. Details at: http://www.cs.sfu.ca/~hamarneh/880.html
304 | INTRODUCTION TO RELATIONAL DATABASES
Focus is relational databases, dealing with relational
database design, relational database languages, and concepts related
to the transaction processing layer (top layer) of a database
management system (DBMS).
445 | ALGORITHMS IN BIOINFORMATICS
involves the application of computational methods to answer or
provide insight on questions of molecular biology. This course
provides an introduction to the design and analysis of algorithms
for bioinformatics applications.
504 | DATABASE DESIGN
Organizing information as relations. Information
retrieval through queries against relations. Storing relations
as data. Efficient storage and retrieval of data needed by queries.
Reliability integrity and security considerations in database
HCEP 511 | CANCER EPIDEMIOLOGY
Collection and analysis of epidemiological data
on cancer; occupational and other risk factors,; analytic techniques
and mathematical modeling relevant to oncology.
CPSC 53A | TOPICS IN ALGORITHMS
AND COMPLEXITY - BIOINFORMATICS
This course introduces algorithms and their application
in bioinformatics Topics include sequence alignment, phylogenetic
tree reconstruction, prediction of RNA and protein structure,
gene finding and sequence annotation, gene expression, and biomolecular
computing. A solid understanding of principles for design and
analysis of algorithms. Some assignments will involve use and
extension of software tools, and others will involve written studies
of algorithms and their analysis.
561 | MATHEMATICAL BIOLOGY
612D | TOPICS IN MATHEMATICAL BIOLOGY - MATHEMATICS
OF INFECTIOUS DISEASES AND IMMUNOLOGY
741 | BIOINFORMATICS
This course introduces the history of bioinformatics,
classic algorithms used in the field, common methods of macromolecule
(ie within areas of sequence alignment, structure analysis, phylogenetic
analysis, etc.) and an introduction to
programming and database connectivity.
MBB 823 | PROTEIN STRUCTURE
AND FUNCTION: PROTEOMIC BIOINFORMATICS
Transition state theory; specificity in enzyme
catalyzed reactions; use of recombinant DNA techniques to describe
and modify enzyme catalysis, the function of enzymes in organic
solvents, and the development of new catalytic activities through
monoclonal antibody techniques.
MBB 831 | MOLECULAR EVOLUTION
OF EUKARYOTE GENOMES
Examination of the dynamics of change in eukaryotic
nuclear, mitochondrial, and chloroplast genome structure and organization
including mechanisms of gene conversion, transposition, and duplication.
Consideration of the origin and function of intron, satellite,
and repeated DNA sequences.
MBB 835 | GENOMIC ANALYSIS
Topics include: structure and function of the genome
with emphasis on genome mapping and sequencing projects, and computational
methods for genomic sequence analysis comprising: methods in genomic
research, construction of physical genomic maps, ESTs - use and
purpose; Sequencing strategies: ordered vs. random; high throughput
sequencing; Collection and assembly of data; Gene finding (prediction
of genes from DNA sequence; Annotation and release of data; Comparative
Genomic analysis; Comparative Genomic analysis; Functional genomics;
Genome organization; Future directions.
MEDG 505 | GENOME ANALYSIS
Investigation of genetic information as it is organized
within genomes, genetic and physical map construction, sequencing
technologies, gene identification, database accessing and integration,
functional organization of genomes from contemporary, historic
and evolutionary perspectives.
890 | STATISTICS SELECTED TOPICS - BIOMETRICAL GENETICS
PATH 531/MEDG 521 | MOLECULAR
AND CELL BIOLOGY OF CANCER
This course focuses on molecular and cell biology
of cancer and consists of a series of lectures reviews combined
with discussions and presentations by students on the topics selected
by the instructors. Emphasis will be on students' presentations
is not an exhaustive list of electives - more are being developed
every term and will be available to students when they register.
part of the requirement for the Bioinformatics training program,
students must take two professional development workshops between
now and the end of their second year. Below is an example
of the kind of workshop required. These workshops are things like
interviewing skills, presentation skills, TA workshops, etc. The
program will pay the cost of this if there is a fee associated
with it. Proof of enrollment is required (please see professional
development workshop form under policy & procedures).
For UBC students, these courses/workshops can be found at:http://tag.ubc.ca/
the Faculty of Graduate Studies Pathways to Success programs:
For SFU students, at: http://www.sfu.ca/careerservices/
can also find workshops offered through MITACS-STEP
If you find another workshop that you feel is relevant (ie like
toastmasters) and it is offered elsewhere, and if it is not too
expensive, the program will pay for it. These are for your professional
development - finding a job or helping with your career after